AI writes a lot of code.
Not all of it should ship.
We're the accountability layer between AI-generated development and your production environment. Rigorous, systematic, and built for the pace of modern software delivery.
Over 40% of code written in 2025 was generated by AI. Acceptance rates for AI suggestions hover around 27–30%, meaning the majority of what gets generated needs human judgment to validate. When AI fails, it fails at scale. Our QA practice exists to make sure that doesn't happen to your product.
Five layers between your code and your users.
Functional Testing
Every feature works as designed. Every edge case is accounted for. Every user flow is tested before it reaches a real user.
AI Output Validation
Specifically designed for AI-generated codebases. We review logic, check edge cases, and validate that AI-built features hold up in production conditions.
Performance & Load Testing
Your app works with 10 users. Does it work with 10,000? We test the ceiling before your users find it.
Security & Compliance Review
Vulnerability scanning, data handling validation, and compliance checks, especially critical for regulated industries.
Regression Testing
Every new release is checked against existing functionality. Speed of delivery never compromises stability of the product.
QA is no longer just a testing function. It's the governance layerthat makes AI-driven development trustworthy. Our QA team doesn't just test software. They validate that the AI-generated systems your product depends on behave reliably, predictably, and safely at scale.
The people who lose sleep over releases.
Building with AI tooling, who need a trusted validation layer they don't have to staff themselves.
Who need confidence before putting real users on their product for the first time.
Who need audit trails and documented quality controls for every feature that ships.
By a production bug that should have been caught long before it ever reached a user.
QA for AI-generated code, explained.
Why does QA matter more with AI-generated code?
A large share of code is now AI-generated, but acceptance rates are low and AI failures happen at scale. QA is the governance layer that validates AI-built features behave reliably, predictably, and safely in production.
What does Ignicube's QA cover?
Five layers: functional testing, AI-output validation, performance and load testing, security and compliance review, and regression testing, so speed of delivery never compromises stability.
Do you provide audit trails for compliance?
Yes. Every release includes documented quality controls and audit trails, which is especially important for regulated industries and enterprises deploying AI features.
Can you test AI output specifically?
Yes. AI-output validation is purpose-built for AI-generated codebases. It reviews logic, checking edge cases, and confirming AI-built features hold up under real production conditions before they merge.
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ReadShip with
confidence.
The accountability layer your AI-generated code needs before it touches production. Tell us about your QA needs and we'll build the safety net.